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arXiv · 2106.05891

Temporal and Object Quantification Networks

Jiayuan Mao·Zhezheng Luo·Chuang Gan·Joshua B. Tenenbaum·Jiajun Wu·Leslie Pack Kaelbling·Tomer D. Ullman
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Abstract

We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.

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Jiayuan Mao, Zhezheng Luo, Chuang Gan, Joshua B. Tenenbaum, Jiajun Wu, Leslie Pack Kaelbling, Tomer D. Ullman. 2021-06-10. Temporal and Object Quantification Networks. https://arxiv.org/abs/2106.05891

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